National and Subnational estimates for Germany

Identifying changes in the reproduction number, rate of spread, and doubling time during the course of the COVID-19 outbreak whilst accounting for potential biases due to delays in case reporting both nationally and subnationally in Germany. These results are impacted by changes in testing effort, increases and decreases in testing effort will increase and decrease reproduction number estimates respectively (see Methods for further explanation).

Table of Contents


Using data available up to the: 2020-05-19

Note that it takes time for infection to cause symptoms, to get tested for SARS-CoV-2 infection, for a positive test to return and ultimately to enter the case data presented here. In other words, today’s case data are only informative of new infections about two weeks ago. This is reflected in the plots below, which are by date of infection.

Expected daily confirmed cases by region


Figure 1: The results of the latest reproduction number estimates (based on estimated cases with a date of infection on the 2020-05-09) in Germany, stratified by region, can be summarised by whether cases are likely increasing or decreasing. This represents the strength of the evidence that the reproduction number in each region is greater than or less than 1, respectively (see the methods for details). Regions with fewer than 40 cases reported on a single day are not included in the analysis (light grey).

National summary

Summary (estimates as of the 2020-05-09)

Table 1: Latest estimates (as of the 2020-05-09) of the number of confirmed cases by date of infection, the expected change in daily confirmed cases, the effective reproduction number, the doubling time (when negative this corresponds to the halving time), and the adjusted R-squared of the exponential fit. The mean and 90% credible interval is shown for each numeric estimate.
Estimate
New confirmed cases by infection date 759 (683 – 839)
Expected change in daily cases Likely decreasing
Effective reproduction no. 0.9 (0.9 – 1)
Doubling/halving time (days) -35 (-300 – -19)
Adjusted R-squared 0.62 (0.25 – 0.97)

Confirmed cases, their estimated date of infection, and time-varying reproduction number estimates


Figure 2: A.) Confirmed cases by date of report (bars) and their estimated date of infection. B.) Time-varying estimate of the effective reproduction number. Light ribbon = 90% credible interval; dark ribbon = the 50% credible interval. Estimates from existing data are shown up to the 2020-05-09 from when forecasts are shown. These should be considered indicative only. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence. The vertical dashed line indicates the date of report generation.

Time-varying rate of growth and doubling time


Figure 3: A.) Time-varying estimate of the rate of growth, B.) Time-varying estimate of the doubling time in days (when negative this corresponds to the halving time), C.) The adjusted R-squared estimates indicating the goodness of fit of the exponential regression model (with values closer to 1 indicating a better fit). Estimates from existing data are shown up to the 2020-05-09. Light ribbon = 90% credible interval; dark ribbon = the 50% credible interval. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence.

Regional Breakdown

Data availability

Limitations

Summary of latest reproduction number and confirmed case count estimates by date of infection


Figure 4: Confirmed cases with date of infection on the 2020-05-09 and the time-varying estimate of the effective reproduction number (light bar = 90% credible interval; dark bar = the 50% credible interval.). Regions are ordered by the number of expected daily confirmed cases and shaded based on the expected change in daily confirmedcases. The horizontal dotted line indicates the target value of 1 for the effective reproduction no. required for control and a single case required for elimination.

Reproduction numbers over time in the six regions expected to have the most new confirmed cases


Figure 5: Time-varying estimate of the effective reproduction number (light ribbon = 90% credible interval; dark ribbon = the 50% credible interval) in the regions expected to have the highest number of new confirmed cases. Estimates from existing data are shown up to the 2020-05-09 from when forecasts are shown. These should be considered indicative only. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence. The horizontal dotted line indicates the target value of 1 for the effective reproduction no. required for control. The vertical dashed line indicates the date of report generation.

Confirmed cases and their estimated date of infection in the six regions expected to have the most new confirmed cases


Figure 6: Confirmed cases by date of report (bars) and their estimated date of infection (light ribbon = 90% credible interval; dark ribbon = the 50% credible interval) in the regions expected to have the highest number of new confirmed cases. Estimates from existing data are shown up to the 2020-05-09 from when forecasts are shown. These should be considered indicative only. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence. The vertical dashed line indicates the date of report generation.

Reproduction numbers over time in all regions


Figure 7: Time-varying estimate of the effective reproduction number (light ribbon = 90% credible interval; dark ribbon = the 50% credible interval) in all regions. Estimates from existing data are shown up to the 2020-05-09 from when forecasts are shown. These should be considered indicative only. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence. The horizontal dotted line indicates the target value of 1 for the effective reproduction no. required for control. The vertical dashed line indicates the date of report generation.

Confirmed cases and their estimated date of infection in all regions

Figure 8: Confirmed cases by date of report (bars) and their estimated date of infection (light ribbon = 90% credible interval; dark ribbon = the 50% credible interval) in all regions. Estimates from existing data are shown up to the 2020-05-09 from when forecasts are shown. These should be considered indicative only. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence. The vertical dashed line indicates the date of report generation.

Latest estimates (as of the 2020-05-09)

Table 2: Latest estimates (as of the 2020-05-09) of the number of confirmed cases by date of infection, the effective reproduction number, and the doubling time (when negative this corresponds to the halving time) in each region. The mean and 90% credible interval is shown.
Region New confirmed cases by infection date Expected change in daily cases Effective reproduction no. Doubling/halving time (days)
Baden-Württemberg 81 (62 – 98) Decreasing 0.8 (0.7 – 1) -14 (-150 – -7.6)
Bayern 135 (112 – 159) Likely decreasing 0.9 (0.8 – 1) -25 (120 – -11)
Berlin 41 (16 – 65) Unsure 1.1 (0.7 – 1.6) 21 (5 – -10)
Brandenburg 21 (3 – 35) Unsure 1.1 (0.5 – 1.8) 56 (3.9 – -4.4)
Bremen 36 (11 – 57) Unsure 1.3 (0.8 – 1.9) 13 (3.8 – -9.5)
Hamburg 22 (5 – 39) Unsure 1.3 (0.6 – 2.1) 10 (2.9 – -6.9)
Hessen 58 (31 – 81) Unsure 1 (0.7 – 1.2) -71 (11 – -8.3)
Mecklenburg-Vorpommern 32 (1 – 58) Likely increasing 2.1 (0.7 – 3.4) 3.4 (1.6 – -12)
Niedersachsen 48 (32 – 61) Unsure 0.9 (0.7 – 1.2) -25 (20 – -7.7)
Nordrhein-Westfalen 177 (147 – 205) Likely decreasing 0.9 (0.8 – 1.1) -35 (59 – -14)
Rheinland-Pfalz 39 (15 – 62) Unsure 1.2 (0.7 – 1.6) 17 (4.7 – -9.9)
Saarland 19 (3 – 36) Likely increasing 1.5 (0.6 – 2.4) 5.8 (2.2 – -9.1)
Sachsen 43 (13 – 73) Unsure 1.2 (0.7 – 1.7) 19 (4.4 – -7.9)
Sachsen-Anhalt 32 (1 – 72) Likely increasing 1.8 (0.6 – 3) 4.8 (1.8 – -7.3)
Schleswig-Holstein 30 (3 – 58) Unsure 1.2 (0.5 – 2.1) 39 (3.2 – -3.8)
Thüringen 33 (11 – 51) Unsure 1.1 (0.7 – 1.5) 120 (5.8 – -6.5)

Abbott, Sam, Joel Hellewell, James D. Munday, and Sebastian Funk. 2020. “NCoVUtils: Utility Functions for the 2019-Ncov Outbreak.” - - (-): –. https://doi.org/10.5281/zenodo.3635417.

“Tabelle Mit Den Aktuellen Covid-19 Infektionen Pro Tag (Zeitreihe).” 2020. Germany: Robert Koch Institut; Bundesamt für Kartographie und Geodäsie. https://npgeo-corona-npgeo-de.hub.arcgis.com/datasets/dd4580c810204019a7b8eb3e0b329dd6_0.

Xu, Bo, Bernardo Gutierrez, Sarah Hill, Samuel Scarpino, Alyssa Loskill, Jessie Wu, Kara Sewalk, et al. n.d. “Epidemiological Data from the nCoV-2019 Outbreak: Early Descriptions from Publicly Available Data.” http://virological.org/t/epidemiological-data-from-the-ncov-2019-outbreak-early-descriptions-from-publicly-available-data/337.